GPU Workstations in the Cloud with Paperspace

If you don’t have local access to a modern NVIDIA GPU, your best bet is typically to run GPU intensive training jobs in the cloud. Paperspace is a cloud service that provides access to a fully preconfigured Ubuntu 16.04 desktop environment equipped with a GPU.

If you don’t have local access to a modern NVIDIA GPU, your best bet is typically to run GPU intensive training jobs in the cloud. Paperspace is a cloud service that provides access to a fully preconfigured Ubuntu 16.04 desktop environment equipped with a GPU. With the addition of the RStudio TensorFlow template you can now provision a ready to use RStudio TensorFlow w/ GPU workstation in just a few clicks. Preconfigured software includes:

Training a Convolutional MNIST Model

The performance gains for training convoluational and recurrent models on GPUs can be substantial. Let’s try training the Keras MNIST CNN example on our new Paperspace instance:

Training the model for 12 epochs takes about 1 minute (~ 5 seconds per epoch). On the other hand, training the same model on CPU on a high end Macbook Pro takes 15 minutes! (~ 75 seconds per epoch). Using a Paperspace GPU yields a 15x performance gain in model training.

This model was trained on an NVIDIA Quadro P4000, which costs $0.40 per hour. Paperspace instances can be configured to automatically shut down after a period of inactivity to prevent accruing cloud charges when you aren’t actually using the machine.

If you are training convolutional or recurrent models and don’t currently have access to a local NVIDIA GPU, using RStudio on Paperspace is a great way to accelerate training performance. You can use the RSTUDIO promo code when you sign up for Paperspace to receive a $5 account credit.

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For attribution, please cite this work as

Allaire (2018, April 2). TensorFlow for R: GPU Workstations in the Cloud with Paperspace. Retrieved from https://blogs.rstudio.com/tensorflow/posts/2018-04-02-rstudio-gpu-paperspace/